{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Image classification using tensorflow pre-trained model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tensorflow-Slim image classification model library provides both the implementation and pre-trianed checkpoint many popular convolution neural nets for image classification.\n",
    "\n",
    "Using TFNet in Analytics-Zoo, we can easily load these pre-trained model and make distributed inference with only a few lines of code."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add the slim image classification model library to $PYTHONPATH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from zoo.common.nncontext import *\n",
    "sc = init_nncontext(\"Tfnet Example\")\n",
    "import sys\n",
    "import tensorflow as tf\n",
    "sys.path\n",
    "slim_path = \"/path/to/yourdownload/models/research/slim\" # Please set this to the directory where you clone the tensorflow models repository\n",
    "sys.path.append(slim_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Construct the inference graph and restore the checkpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from /home/ludviq/workspace/checkpoint/inception_v1.ckpt\n"
     ]
    }
   ],
   "source": [
    "from nets import inception\n",
    "slim = tf.contrib.slim\n",
    "\n",
    "images = tf.placeholder(dtype=tf.float32, shape=(None, 224, 224, 3))\n",
    "\n",
    "with slim.arg_scope(inception.inception_v1_arg_scope()):\n",
    "    logits, end_points = inception.inception_v1(images, num_classes=1001, is_training=False)\n",
    "\n",
    "sess = tf.Session()\n",
    "saver = tf.train.Saver()\n",
    "saver.restore(sess, \"/path/to/yourdownload/checkpoint/inception_v1.ckpt\") # You need to edit this path to the checkpoint you downloaded"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Export the graph as a frozen inference graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The export_tf utility function will frozen the tensorflow graph, strip unused operation according to the inputs and outputs and save it to the specified directory along with the input/output tensor names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Froze 230 variables.\n",
      "Converted 230 variables to const ops.\n"
     ]
    }
   ],
   "source": [
    "from zoo.util.tf import export_tf\n",
    "export_tf(sess, \"/path/to/yourdownload/models/tfnet/\", inputs=[images], outputs=[logits])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load to Analytics-Zoo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "creating: createTFNet\n"
     ]
    }
   ],
   "source": [
    "from zoo.pipeline.api.net import TFNet\n",
    "model = TFNet.from_export_folder(\"/path/to/yourdownload/models/tfnet\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test it on one image\n",
    "\n",
    "![Test Image](./test.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import json\n",
    "im = cv2.imread(\"test.jpg\")\n",
    "im = cv2.resize(im, (224, 224))\n",
    "im = (im - 127.0) / 128.0\n",
    "im = np.expand_dims(im, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "with open(\"imagenet_class_index.json\") as f:\n",
    "    class_idx = json.load(f)\n",
    "idx2label = [class_idx[str(k)][1] for k in range(len(class_idx))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Persian_cat\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "result = model.predict(im).first()\n",
    "print(idx2label[np.argmax(result, 0)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fine tune on the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Construct the inference graph and restore the checkpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from /home/ludviq/workspace/checkpoint/inception_v1.ckpt\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()# if you want to test your code, you can use it to reset your graph and to avoid some mistakes\n",
    "\n",
    "images = tf.placeholder(dtype=tf.float32, shape=(None, 224, 224, 3))\n",
    "\n",
    "with slim.arg_scope(inception.inception_v1_arg_scope()):\n",
    "    logits, end_points = inception.inception_v1(images, num_classes=1001)\n",
    "\n",
    "sess = tf.Session()\n",
    "saver = tf.train.Saver()\n",
    "saver.restore(sess, \"/path/to/yourdownload/checkpoint/inception_v1.ckpt\")# You need to edit this path to the checkpoint you downloaded"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove the dense layer of the graph(Inception_v1) and export the graph left as a frozen inference graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The export_tf utility function will frozen the tensorflow graph, strip unused operation according to the inputs and outputs and save it to the specified directory along with the input/output tensor names. In this example, we use the \"AvgPool_0a_7x7\" as our end_points, freeze the graph accoring to \"AvgPool_0a_7x7\" and export this graph to the specific directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Froze 114 variables.\n",
      "Converted 114 variables to const ops.\n"
     ]
    }
   ],
   "source": [
    "from zoo.util.tf import export_tf\n",
    "avg_pool = end_points['AvgPool_0a_7x7']\n",
    "export_tf(sess, \"/path/to/yourdownload/models/tfnet/\", inputs=[images], outputs=[avg_pool])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load to Analytics-Zoo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load that freezed graph from the specific directory above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "creating: createTFNet\n"
     ]
    }
   ],
   "source": [
    "from zoo.pipeline.api.net import TFNet\n",
    "amodel = TFNet.from_export_folder(\"/path/to/yourdownload/models/tfnet/\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add the layers you want to the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use sequential model to build your model. First transport the type of data from NCHW to NHWC. Then multiply the scalar and make the input and output both contiguous. Add the freezed graph and add a linear layer after the graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "creating: createSequential\n",
      "creating: createTranspose\n",
      "creating: createMulConstant\n",
      "creating: createContiguous\n",
      "creating: createView\n",
      "creating: createLinear\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<bigdl.nn.layer.Sequential at 0x7f25a4766490>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from bigdl.nn.layer import Sequential,Transpose,Contiguous,Linear,ReLU, SoftMax, Reshape, View, MulConstant, SpatialAveragePooling\n",
    "full_model = Sequential()\n",
    "full_model.add(Transpose([(2,4), (2,3)]))\n",
    "scalar = 1. /255\n",
    "full_model.add(MulConstant(scalar))\n",
    "full_model.add(Contiguous())\n",
    "full_model.add(amodel)\n",
    "full_model.add(View([1024]))\n",
    "full_model.add(Linear(1024,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### get data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the path to the cat_dog data you downloaded. Then read these images from this path.\n",
    "Use udf functions to design some functions to mark data.\n",
    "Seperate the data into two groups: training data and validation data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "creating: createDefault\n",
      "creating: createSGD\n",
      "creating: createSeqToTensor\n",
      "creating: createSeqToTensor\n",
      "creating: createSeqToTensor\n",
      "creating: createSeqToTensor\n",
      "creating: createSeqToTensor\n",
      "+------------+-----+\n",
      "|        name|label|\n",
      "+------------+-----+\n",
      "|cat.4304.jpg|  1.0|\n",
      "|cat.8362.jpg|  1.0|\n",
      "| cat.711.jpg|  1.0|\n",
      "|cat.4361.jpg|  1.0|\n",
      "|cat.1491.jpg|  1.0|\n",
      "|cat.4931.jpg|  1.0|\n",
      "|cat.5675.jpg|  1.0|\n",
      "|dog.1551.jpg|  2.0|\n",
      "|cat.7118.jpg|  1.0|\n",
      "|dog.1495.jpg|  2.0|\n",
      "+------------+-----+\n",
      "only showing top 10 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "from bigdl.nn.criterion import CrossEntropyCriterion\n",
    "from pyspark import SparkConf\n",
    "from pyspark.ml import Pipeline\n",
    "from pyspark.sql.functions import col, udf\n",
    "from pyspark.sql.types import DoubleType, StringType\n",
    "from zoo.common.nncontext import *\n",
    "from zoo.feature.image import *\n",
    "from zoo.pipeline.api.keras.layers import Dense, Input, Flatten\n",
    "from zoo.pipeline.api.keras.models import *\n",
    "from zoo.pipeline.api.net import *\n",
    "from zoo.pipeline.nnframes import *\n",
    "image_path = \"file:///path/toyourdownload/dogs-vs-cats/train/*.jpg\"\n",
    "imageDF = NNImageReader.readImages(image_path, sc)\n",
    "\n",
    "getName = udf(lambda row:\n",
    "                  row[0],\n",
    "                  StringType())\n",
    "def label(name):\n",
    "    if 'cat' in name:\n",
    "        return 1.0\n",
    "    elif 'dog' in name:\n",
    "        return 2.0\n",
    "    else:\n",
    "        raise ValueError(\"file name format is not correct: %s\" % name)\n",
    "getLabel = udf(lambda name: label(name), DoubleType())\n",
    "\n",
    "labelDF = imageDF.withColumn(\"name\", getName(col(\"image\"))) \\\n",
    "        .withColumn(\"label\", getLabel(col('name')))\n",
    "(trainingDF, validationDF) = labelDF.randomSplit([0.9, 0.1])\n",
    "labelDF.select(\"name\",\"label\").show(10, False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Show the distribution of Training Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----+-----+\n",
      "|label|count|\n",
      "+-----+-----+\n",
      "|  1.0|   64|\n",
      "|  2.0|   59|\n",
      "+-----+-----+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "trainingDF.groupBy(\"label\").count().show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using pipeline to train the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Combine the preprocessing processes together. Before identify the classifier, design the summary's name to make sure we will store the log in right name. Identify the args of classifier. Then save logs to the dirctionary. Use the classifier identified just to create pipeline. Use this pipeline as your model to train the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "creating: createRowToImageFeature\n",
      "creating: createImageResize\n",
      "creating: createImageChannelNormalize\n",
      "creating: createImageMatToTensor\n",
      "creating: createImageFeatureToTensor\n",
      "creating: createChainedPreprocessing\n",
      "creating: createTrainSummary\n",
      "creating: createCrossEntropyCriterion\n",
      "creating: createScalarToTensor\n",
      "creating: createFeatureLabelPreprocessing\n",
      "creating: createNNClassifier\n",
      "('Saving logs to ', 'classification cat vs dog20180727-113318')\n",
      "creating: createToTuple\n",
      "creating: createChainedPreprocessing\n"
     ]
    }
   ],
   "source": [
    "transformer = ChainedPreprocessing(\n",
    "        [RowToImageFeature(), ImageResize(224, 224),\n",
    "         ImageChannelNormalize(123.0, 117.0, 104.0), ImageMatToTensor(), ImageFeatureToTensor()])\n",
    "\n",
    "from bigdl.optim.optimizer import *\n",
    "from bigdl.nn.criterion import *\n",
    "import datetime as dt\n",
    "app_name='classification cat vs dog'+dt.datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n",
    "train_summary = TrainSummary(log_dir='./log/',\n",
    "                                     app_name=app_name)\n",
    "classifier = NNClassifier(full_model, CrossEntropyCriterion(), transformer)\\\n",
    "        .setFeaturesCol(\"image\")\\\n",
    "        .setCachingSample(False)\\\n",
    "        .setLearningRate(0.003)\\\n",
    "        .setBatchSize(16)\\\n",
    "        .setMaxEpoch(9)\\\n",
    "        .setTrainSummary(train_summary)\n",
    "# BatchSize is a multiple of physcial_core_number(in your bash command)\n",
    "\n",
    "from pyspark.ml import Pipeline\n",
    "import os\n",
    "import datetime as dt\n",
    "if not os.path.exists(\"./log\"):\n",
    "    os.makedirs(\"./log\")\n",
    "    \n",
    "print(\"Saving logs to \", app_name)\n",
    "pipeline = Pipeline(stages=[classifier])\n",
    "trainedModel = pipeline.fit(trainingDF)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test the model on validation data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the vallidation data to test the model, and print the test error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Error = 0.0714286 \n"
     ]
    }
   ],
   "source": [
    "# predict using the validation data\n",
    "predictionDF = trainedModel.transform(validationDF).cache()\n",
    "# caculate the correct rate and the test error\n",
    "correct = predictionDF.filter(\"label=prediction\").count()\n",
    "overall = predictionDF.count()\n",
    "accuracy = correct * 1.0 / overall\n",
    "\n",
    "print(\"Test Error = %g \" % (1.0 - accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make the pic can be seen on this page. Repeatedly run this code without restart the kernel will result in a warning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/IPython/core/magics/pylab.py:161: UserWarning: pylab import has clobbered these variables: ['f']\n",
      "`%matplotlib` prevents importing * from pylab and numpy\n",
      "  \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n"
     ]
    }
   ],
   "source": [
    "# get a pic about the result and make it can show in GUI\n",
    "import matplotlib\n",
    "matplotlib.use('Agg')\n",
    "%pylab inline\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import imshow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Show the change in loss using a pic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'loss')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# read loss from summary nad show in pic\n",
    "loss = np.array(train_summary.read_scalar(\"Loss\"))\n",
    "plt.figure(figsize = (12,12))\n",
    "plt.plot(loss[:,0], loss[:,1], label='loss')\n",
    "plt.xlim(0, loss.shape[0]+10)\n",
    "plt.grid(True)\n",
    "plt.title(\"loss\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Collect two examples from both sides."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "samplecat=predictionDF.filter(predictionDF.prediction==1.0).limit(2).collect()\n",
    "sampledog=predictionDF.filter(predictionDF.prediction==2.0).sort(\"label\", ascending=False).limit(2).collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Show two cat-pics and their predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prediction: 1.0\n"
     ]
    },
    {
     "data": {
      "image/jpeg": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAoHBwgHBgoICAgLCgoLDhgQDg0NDh0VFhEYIx8lJCIfIiEmKzcvJik0KSEiMEExNDk7Pj4+JS5ESUM8SDc9Pjv/2wBDAQoLCw4NDhwQEBw7KCIoOzs7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs7Ozv/wAARCAEkASwDASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwDkNvGKp6kuLYH0YVoEDHFU9SX/AERvavSlsci3HxfdX6VYUZ4qCADyU+gq0mMCnHYTENZ8Ixqso9q0SBVBQBq7e60pboaLwHOaeRxQq0pHaqJM7U1/0U/UVYi/1an2FR6ioNm/tUkH+oT6Co+0V0JlwRQy/KaVBTmX5at7EmbpQxDIPSQ1ohao6UnyTD0kNXwDUx2G9xrAAVXtYd+qOo6sABTL6fy14NHh1pJ9biB5zXPXmlobUo3Z3llaQQQBBCvTkkZzVPUdCt7hS8SrFJ7DANbcMH7sEgUPFx2riXuu6Otq+jPMJbeS21xo5FIIWrOOc102t6ZHcHzlTE0Y4PqPSubIIODxXfQnzI46seVkLjBrOQY1RuP4a02AqiqY1X6rWsjNFnbx0pyjFSbKXaOtUIoQD/Tpsegq5iq1uP8AiYTfQVc281ERsYyZU7aj0zTZbn92ilm3HpWlYWpupxFGMk9faus0/TIrCDZEvzH7zetctarraJ00qel2c1N4L8+DMtwEI5CgZrjtRsmsL5LZyCUOMjvXrssP7s5rznxjGE1eEhcAis4yk3qVKKS0M3UEV5YFYZB61EIIo9VVEQBeOPwqe9H763psgxrCn6fyrotqYX0H6qgFpwAOam04f6ElR6r/AMen41Lpv/HotafbJ+yFx95v+uTV03hNP+JKn+8a5q8O0/VGFdX4TXGioP8AaNc1X4jaGwurNmaOP0Ga4LUh/wATCb/er0HU1/0tf92uCv0zfzcfxVkWdDt71W1Bc2r8dqu4FV70Ztn+lenLY41uRW3+oT6VaQcVUtP+PZKugcUQ2BjTVAj/AImo91rSIHpVFxjVEOOq0pDReUHHIpSM04Digqaskp36g2cg7gUlvzbx/SpLwf6LJ9KbaD/RU+lR9ofQmTipDjFNQDFOC8Yq+gjP0vhrgf8ATQ1odapacuJbgf7dXwMVMdhvcxdU4q/4Qj3a3D9DVLVl4qx4Xufs+r28h6BsGvPxG52UD1sRBYEGKiMY7Yq5EvmQbfxFMEXODwaxexuzKu7QkZwM1yOtaYYSbiNTtz849K7y4iKqeaxJ/LMxjcAq4wwNKNR05XJlDnVjhttVAoGqr/uVranYva6hJDbgvGOVPtWO5kjuRM0ZAHy13/WKbW5yeymuhoFcdqTGO1MilklzsjJIppnIHzRso+lWq9N9SXSmuhWi+XUJcjtVuJJLq4WGEZZjjioIreWa9+RTukwAK7bR9Kg01VHD3D/eb09hXHVxCtaJ0U6PVk2kaQljCFXmRh8zVtJCAuKmt4VVRxVgQoRmueK6m7KRtsggjtXmvjmIJqFtj0NetFAkLMR24ryTxzMJNdhiA+4pzW8NzOexi3gzLAabIP8Aiag4/u/yNTXQzJD9KRxnUc+mz+tdRyiaqP8AROncVLp4AtV+tM1Uf6IPqKmslxAPrWn2yPskOpHAQ+oP8q6rw1cwwaQqyNghjXJ6qcLH9a0bMFbGdc9DXNWXvG1PY6ktb31wNpOcdq4jUYQmp3KjkCQ10fho/OnPY1j3ybtSujj/AJamsTQ0B0qK5UGFvpUwxio5eYmHtXpvY4luVLQYhHsaur92qsC4SrY6UQ2HIb2qpKP+JhE3tV01VmT/AEqM+lKQRLq9KUgigdMYpeBVklW6H+jv9KbZjFsg9qnnwYmGO1RQJiPHoah/EV0JFGKfjjpQB6UuCKtbElKwAE04H941dxVa0XEsuOPmNWsVMdinuZWqp8pNUNMlMV8hHTNa+oqDCfpWJYyLHeANXDikdVB6nt9hdBrKJ24O0VaZ1Y5DDNY+mzxTaVbMhBGwZxWioQ4INcKkdjQTrlckjFZV/HbhSSMmr95KxXy0ADDvWc9tv5wSe9RKQkjNe2jlUbV496zo9GM4mimAVN2VY108WlNIwj5GegoksTaXzLK2V2Hbn1rNuxaVzkpdIFteNCkhCkYyO9SCEm9S3MY8pB3HWuj06yW6vDLJjCc81ImiMDLPJ8oYkqPShO4WMJ9PgWdTEdhHQmtqy09vlk8zeR3pn9n7iCR8o71atfNtxhRlR2pp6iNW3+QAMelWQATk9KghdLhOBgjtSyBs7QeB3rdPQi2oXsypAzMcKorxHW7k33iGSUnjcwH0FexXiGa0kRj1HSvG7qMJr8qEcB3ropO6MqumhJKu6SL2WkKZu5CO2z+tSkpvTJHCHvQhUy3DAj/lnj8zXXocpBqaj7MP94VIs8FuCryquD61Ffxy3EsUEZxk7j+FLPpEElzIz5yTVNvn0FZcupU1G9t5fLEcgYg1rR6jp8BltZ5WR2OMBaxdSsYbVYzGuCW5qtqYB1uQE4G8Vz1b31NIWtodnY6rpumYInDEA/ebFZyXCXss9whBV5TjFFomiwQyNeGPeHUYYc+9P09YpYpXt8CIyttwMDFZI1RcLqD94fnSNJHtI3Dp61n/ANkZz+/emnRlx/rn/OvRvPscdl3LgdFGNwHA71IJ4sffX86z20xJlUs7DC44NKuiRY5d/wA6mLnbRDaj3L5uIB1lUfjUbTwvKCsinCnOD0qr/YltnkufxqaHS7e3kOAcOpByaUuca5Sz/aFovWdPzpj6nZjpOn51EmhWWMmP9aUaLZj/AJYjFWucn3RH1SzKkeevNObULS3AWSUKSAcU1tHs1UkQinGxt5wrtGGIQDJqXz3H7thg1mxXrMPypTrdgBxL+lOTTbPvAv5U/wDs+0HSBfyqv3nkL3SCLUraOA3Bc7GkIBx7Uf29Y/3m/wC+alht4SrQ+WuFlyBj2qYWVuDjyl/KlFT7jfKZl3rdpJGQoc/8Brn2ul+0K6gjFdnJZQFCBEo/CuX1GBIr1QAME1hWi7XZpTa6HZ+DdbPkvBIx2Dlfau0ttRVoyeMV554Zji3bSOa7WNYwoROMelePUnZ6HpQV0aUQebhe/etiy0squW5Nc3qAvrLT4zbNskmbaCRzWUl/d2t0pj1GeWQfeG7+lSmluJ3O9t49l8VZeR0qLULH7TcmU9FXFS6NeGdkW5ALsuUfHWrM6eVcMpP3ulUknG6FezMfTNKWJmTnk5rT1JEjsHwOi4qS0UBmJGKs3YhgsZJJ+VAzzThHcUpamFpVm01iSw61XmtJ4udvGaxdW1fUYioS5NtDn5Y4xzj3pbbV9SgjErSC6hJwwYciocopJXKV2zYinWFgTx60t3epnO/bx0zVC9vIS6iQGN2GQDWbqCSSxExScjt61UZdAtbUr+KvEZ0/T/JiOJ5+FI7D1rzOMNLqjrdSyIvO4s3OcVteImkZozISSCMZ7c1nmCOTWMyDcGY5B+lehCFkck5XYi2tkW5uSRtPV+9TQLaReZiQN5ewqc+5zTxp9qrqDEOUNUZPssYudsJ2oyDHfvmtmrGS1Ld3dBJ4J4WDgNg4PrWnMQZ5CP71ctD5TXICBowTnDGr15qcttezqiZXf1NONT3rsTh7tiTW+kP+9Wfqrsmq3JUD7/cUXWoNdSou0FARg4qTUvO/tK42Rjb5hwcVM5c7uiorlWo2LVzG297ZJGP3iw610GhSmawaTYE3SE7R26VztpC93cLFIowOSQK3NKYw2zouMCRqzUWy7muBx0xSYp1APavSOMrRHkrjpUyqSAarRcXUq+lXEPyYqYbDkAAFRXOF2H3qYAVXvziEH0IolsC3LKA7R9KUdxRHyi/SnEYq1sSxjD5SPaq1qcqw9OKtEcZqlZNlpV9GNQ9ylsWlA5FPI44FMA5qUGrRJRibF9In0P6VdBqkONXI9Vq9jFREpiSj5DXJatkXaH/arrnHymuV1ldtzGf9qsq+xdPc0tAu1ikKk812Gjakn28K3K5715tAzRzqykjmu20T/Uliw3HkV4tanqenTnoei63af2hpcU9uCZLc79o7iuLFjFJfGdJvLAOWHQ/Sug0rWWjQfvBgcEVtRDSbz5rizjLnqwHWsWr9bMrY5231wJf26RfdRgCfauuvpleaNl67a5rxTZ2sTW4sYki2nPyjFWLG5aYDc+5lFF1T90rl5tTctphjDcHNZPjfWPscFtbj7sjAtj0FMN8FchjtIpuxNWuI2nVZEQ8Z5oVZJNdw9nrcxb63GpxpPbSDOMH6d6t6dZfJHaINzuQWx/CK6ttH0iJMrAE9QvFU557aziYWqpEO5HWl7J6XloSmuiMXxmlpFYgcCRBwR61y+n6gWtfnPPvWrqol1NyoQeWuck+tcfLPJZs0LxkbDxW/LzbCvZalLxLKHcsox8wrI86f7dmNV3buC1XdXkMlv5hHJYVSDf6Qrf7Veko8qSOFu7LEj6ipTKx5IOKizefaHV4kEh24+vOP61euWOLc+1MlJGqgeu3+tacpHMZ13aTRyRyzMDIzdFGAK0ZokurWYAAuGPbvUWtblETr2aobO8jguphNIAjnpTdoTFrKNzKQlZdhH8Wa2tQl/cN6vKefxrLu1jN2ZIZFKk5FW9RnRo9qsN6uSR+NZppNltNl60jEFzIhIY4GCBUlngRyf9dDVSwZ9ytJ95hmprdsCQf9NDTewludDjikxzTsEUcg8V2nOUB8uoOvqKuoPlqk/Gq49Vq6nQ+1TEpiqMnPaoNRGLNiO1WFz2qDUF/0GX6ZoewluT2xzbRnHValIqCxIaziIH8IqxjtVLYT3IznPFZ9lxdXA9GrTIwOlZdr8upXK/SpluhovCnDimg9qeDVokoSHbrMY/vLWn7Vk3JxrVt7gitRpVjGSRULS5TWw7AxXO+IIdpjbH8VXp9VG4rCu5qzLuae5+WVRgc1z1a0LWNqdKe5QYjPBHFbGnXk7QrErY5xmsqWL5DhVBFTaNJifYzYGetcEnzI6orlO30+0vIsPFKHPcGuy06Rvsylhz0Ncfp7TRfMMgDv2ro9Htze7pFvGQKfu471zSj2N0+5b1ezbyfMClmqhpNwiW8zswADY+ldpZWatDsmZZMjGcVxvibRpNCcXNm2LaR/nU9jS9jdczLjNN8pmaje8kxk4J64re8MI7RKWBAIzmq2i6Q2t43MPLByeK7NdLjs7NYbfCbehNZeyctUVOaj7tytcbihAGTXHayb4uwhQgA9cV1M1vPHuk+1qcc4xWPdrLOn31A9RW6WmpjfXQ5L+0b20mVJ1O09ao6sxvVabyyijpx1rql0NZ5N7gkDuTWbrtuI0MagBFHSqo/GKpblOC1YYs/xFZwPzKfetXWwgtCORyKyAreWrAZGe1epPc4I7GrdkhLakmz/AGxGPUL/AFpt6wWC1J4FNmmjbV4mWRSoAyc1VxEusD/Qz9RXOzkidsetdDq7o1mdrA8joa56ZWMpIBqK71HS2IxncPrXTWdjAyNOUBZmPWuawdw46V12n82I+p/nU0VdjqbGew2XiAepFMjfaZB/tmpbkY1CL3qso+Z/981cxROwGMU0nHan4FIR2rrMDMm+XVoj0ypq6uec1SvgBqVsfXIq8RhqhFPYeoyOKZeDNlKP9k09PpRMM20g/wBk03sLqQ6Yd2nxH2q3zmqOjMTpyD0zV7JFOOwnuKRxjNZMI26zOPVQa1CRms2P/kPOPWOlLoNF3pTscU1xtqKa5WGEsxovYLXKGots1C2cDpmpJg0g3uSB6VRST7Tc+Y+SAeBWmEVoxknJ7CvLxFdt2Wx30aSSuynHHuzhP0pv2V/My3HoDWkIQqDA57VDO6RDnDuffgVypm7M+W2GCXYAd6XTpLZJ9saBj61XvGeRsk5AqbSY1N2oYda22Rl1Ov08G6zFMwUgfKBXQ+H7ltLleO5YCHOdxFc7aW3kkSLnd1ArdgnhvoRDJhsdSPWpstyjtoNUtjGGjcEUzUJhfWbwiESBhjDCuQSGS1mVoWLIP4TXQWOsRlNsoCkVaaItYfoHmaba+S1v5fPJHetoX0bjkis06jE6fJgms+Z5JX3gFR7UaJA9S9fyxy5RFB3cZAqoun7MM7Ae1PicD5cYJ70pRifnbIrKRcdCCaZVUpGoz6g1y2s2jsr/AL3OedtdROgRSUAzXOalO6o/mR49+1EHZhLVHnevRlLNgVwQ1YgYrChHHNdJrLq9q4GBz91ulYEsI+zrtG056GvSk7nGlYv6iN9pahu+Kgms4BqkUATajgZAqxf8WtoPcUt4P+J3bY4O0VVkySrqmlw20PmRs3B6E1ktNKrkK5A9q6LWv+PI/WuaYfOaivFJ6FU22tR4lmcFS3A56VoxSamkWYM+Xk4wKz4RlZG9BXVaXzp0Yx6/zqaUbsc3ZGEbi9knUMi+Z2yMU1Gkwd33txzj1rRvht1KI+1Z4GWf/eNVONmKLuduKXvSCnd67TnMzU/lurVv9ursgxyKp6wAFgb0kFXXHyfhULdldB0YwKkdQY2A9KZCcrUh4XpT6CM7RBmyYf3XI/WtAD1rO0Q4jnX+7Ka02NEdgluMIrMb5NeTn70ZrV/h4rKuDt1y3J4yhFEgRfc/KT6VgX0vnTbQ3yjrVzU70lxBEeT1xVIwqgVf4mrjr1fso6aMOrJ7VFaUfLwO1XC5EwVAB6CoLKPZuyhB9anhCJLvPIz1rzmdaJSSr7cdetVrmHawOwkfyrTgEM8m77oHr3qze2yPbkqBwKlblPY5khRkYHTNFpiO8Q9OakeJRL977o6Gq8YP2gc8mtSDsY5DFDlwTuGAM9qltZYrHEjDy1PTJqC3IeBZFUlcY5qe2SGfMc0XmEnjd2qb9B2Ni11KGcAIwrSihicA8Vzz6a1vg2ihm9OwqaO6urMh5jkk7VUVN7Dtc6uG3iTpVtYk284xXPw6g42mQEZGaurqikYqlURLgy4VXle4qlPcqAUZwkg9e9Qy35Zt0fUVVmKXZ2yr2qHNFKJRv9TvEyEAYeo7VjvPdXJZZEOT+Rrdl0yNEJim59Caoy2NyIyy44FSpFWOI8Q2zLbyAHGOTXPiQfZEVz/FwfSuz1ayEllKS2Djn61w8y7IFUdnrvpS5kck42ZtX4BtbUE8gjBpLw/8Ty1/3RUd22bG19mFSXh/4ndqfYV1IwHa2MWTfWubY/Ma6TWz/oTfWsaPS554lljwQ36UVouUtAptJaldHCwuvdiK6rShjTovoa5+5002cal2BZh09K6DS/8AkHxD2ooxalqFRpop6lxqEFZykbn/AN81o6mMX8FZkfJf/fNKruOGx3Kgg07NFGBXWYGbrgxZhv7rg1cH+qX3FVdbXOmSn05qzBzbRt/sioW4+gsLdQe1T9Riqkj+TMD2araEEcU0wMzSBtuLxPSWtTBzWZpwK6tfJ7g1pkgHrRDYJbiZrG1hjHfW8g7ZFassqRIWLAfjWPPcGeTzCucfdFYV6ijE1pQ5mRLblVaRyMnmi2iZpQ5+6OmakUGXAc456VLOjRBSCcdq8zmvudtrEkTsd3OPwqSOANjIIPXNQGNwwdSQCOSKv25Z4dqipY0y5YxhFOTgeuKm2CYsqElT6020Q7ShXrU0YEM5xnJqSznb63FtKwHPvVBIwJQ+a2taCoQzEEk8Cs6OMSRNgAYq0QdHo+Db+WXyO1WXs5bdjKzEIew6msvQ5zG2xugNdfEI5olD4IxUFlXTdRgVCXPI7dhWlFFbXkguJSMLyorDv7BnQ/ZxtRTk4HWqdqNTIcPmOIDlj6UtGB1yG2nd9uMLxT0togmPU1yFjrqxkwKhPzda0U8QByyqMYOFrNopHSRWsUZYADkVDIiAkACs+PVJNgJXtVe7ubhm8yMnHep0HqSXDwQk7xn8apT6hlMWzMf9mnSWFzfx7gSKsadpDW2TKCPeqT7CaOb1m0cadLJvZSy554rzq4kO3ZgkButexeIYBcWMiBQcL2ryS9KwSMjx5XNdlF6HNU3LdywNla4/vCp7wY1y0HsKo/aYpLeJDlArA5Iq3JcRXOtWbRNkDg12xkjmaJ9aQiyejSf+QfHVjWVH9nyZqLSB/wAS6Ouj7Zn9kpa2cKgPpWjpfFhF9Kz9fHCGr+mHFhF9KS+MH8JU1X5byA1nQ8h/981pa2MGFvQ1nWv+rb/eNZVty4bHUnWrROCxP0FRHXrcfdRz9BVhNPth/wAsl/EVOltCmMRqPwra0u5neJjX+sfaLOWJbZwCvXFFtql4bSNYrRmAUDPrWxdxJ9jlUKBlD2qHRwH0qA45C4qeV33KurbGZLc6rOhBtQAPWmRXGsEiMMsfpmt9xnjFULiIhsA4PUGm4+YlLyMy3ttSfU5o/tASQqCzDvV06PeEZk1B/wAKZp9wTrzCThvKwa176XyYGYEDipUY2uym3fQ5qa2ZLoRi4eTHXJrS8nbAoHUDkmoLVAztI5HrVvzFkjOCcDjNebUfNI7IKyEQb4MImSO9SBHkh2yMQV7U/Ttgfbk4+nFTzGKKbcBwD371kaEUQzDjOT6mrFsAi89B6UscQkQtgeuR0pvlkA8nApiNC2O47VHXpU7QNEGbd0HeqenvslALH2rVcK/yHqfQVm73LRyepEvMA3NMSPy06EjPOKn1hVjuRj7ucfWnWkimJgcfjV30J6lnSJl84gqAM9xXW2hG7AAHtXO6fBHInCc9q6CyjyFH3SlSMnMjRggjqfyqwiRXUHlP0bqagd4w2xj/APXpoV84iPArNjRKmh2MZJSMDjrTv7As+CF5p6SNGvzA1dictGCOtQyiqumxQR46j3oW2jAAIGDVqUEx5NRFCzjB4pco7ksSCJcIBxTZpH54xU6JgGoJQcHFVFWJZz2o8syox56ivMPEsIiuGwuAWz0r1G/hkAaUcZPOa4TxZCssPmqASDziuqjKzsZVFocuhzaxj0kFassSR65ZeWgXcMnFZP3JET/bBrauP+Q9Y49K9CJxst6z/wAg+X6VHpI/4lsX0qfW+LCX6VFpGP7Mi+lb/aM/smfr/wB1Ku6eMWMP+7VLxBwqYq/p/wDx4w/7opR+MJfCU9dyIY29DWfZf6k/7xq/rp/0dPrVCw/49z/vGs63xFw2Ow305Ze2aDHkcUwxEc10GRLId0Lj1U1T0B86aoz91mH609/MQH0qhoU+y3lQnpK1TfUq2hvlQRx1qvNHvXpyKcky460/Knoap6k7HM3Mbxa0rISGKcYq61w95H5ZHK/epmrYi1O2cdwRUmnR7y5VT83WuGtNwVkdVKPM7liC1DwkEYxVdLdgWxnOeBWnjygCzAHGMVHFbyF/M4xnj3rgudVhLa0kQB+AO/NWriFDEM804Ow4IAxUqwF03jkjtQBHaIQvy/dHUVYkQEDA25/WoVcoREuct1NMluQjbCeBwDTQmWIUWOTB6noauRqxJBPOPWs1HCkfOST0q1bPuYhiVJ7e1JoaZi67hTn8qbYxo22TPB7Gm69IQ+MHrxRZW8skSAMOn5U2tAT1N7Tn23JjH8XT6VsqDATICTmsC2tpIgkufnB9e1b9pcQzDax+Ydql6DJIr2GU/vOSemamUqgzG4YZ5qO4trfZuDKpqrgRRgK+8+1Q0UjWjlWYEVdtsD5cdBXMLeyWxC88mtmxvtxJPcYqeVhc1JQWwBSxwY5PWo1lHGeMCplkDJwetOwrjZrhIk5PSqZu1c/KcGmyRbpWV2ODVPKwTbcZA6GqSESXkDSRZPAPWuK8SWMUlq5jGCvXArs5bsY2qfwNZ19bxmwl3oBuBq4uzE9UeQXqbL6IfStWU51yy+lVtaSGO6Dru4PpTllWbV7FlPavRizjkjW1viwl+lM0kf8AEsh+lSa5j7BL9Kj0k402L6V0/aRj9kzvEXCoK0LIYsocf3BWf4h+6laFnxZwj/YFEfjCXwooa7/qEHvVLTx/o/8AwI1c13Pkpj1qnp5Atun8RrGt8RcPhOwSXAxUgdWHIqsOVBHpTgSK6rmVidlG01haRFue7TptlNbAc46Vl6YTFqd6hHVgah2uilsWzFKnApBLMnVTV7zBjpTDtPWqsK5g6pMZp7cAYYE1r2uYbdCnQdfesfVz5d1G/bOOK2LML5Gd3avKxN+c76FuUkkDXEwIH3aupti2gj8Kqw5jG7nk1MSspxnOPSuU2JJVzl15FRw3u0sH6dKI0kRCJM8+9RpYrI+4NxnpTuKxZKxyZ+fk1WkQkgMOBViS0KYZW4HpTDMEBBTcTU3KsMj5lBzn2NX4GUnjlhWezfNnp6mlWXy0bYSM9TVpkNFLxNODhUXDHpT9OuPLs1SYheOCazb2V5r9Wl6LWzHbwXUCLwCPerZKN7SEhaMK0nmE81Lc2kiE+Uu0HvVSwuodOi2t1zj61qQ6nFOu0AYJrNloghihBUSyliOxNaAih2hlGBUY06O5O8HGanWz+zR7C24UlqDB7COQKSM1LHAkI+UcU+J8DaRUuN3StLEXJUXeARUhIjXjtVNZTCxUnimvPu6GhRC5JPMCu6seWYvIcEipL25wNi9RWRNK6uCeBQ0CNuCML87ZNU9Wvwls6Lg5GK5668RyWxMaSlWHTPQ1WtNSvNTuNkiKRnqtTa2rK3Oe1+0nEoLRFQ3Q1n6aSusQKx4RsCvSdf0r7RpCyIgMkY6V5hclre+342spzXVTndGE42Z02ut/oUgHpTdMX/iWw/SqFzq8V3prxtgSba1tMUHSoT/s12pqUtDlastTJ8QL+6Rver1scWsX+4Kq+IR/oqH/AGqtRDbbRH/ZFNaTBq8ShrOMRDtmsmCYxoVHrWnq7f6r61ibsE1nW3LgtDv7UB7SJ8dUB/SpCnHSsbTdSK2cSspwFxmtKO/hfA310KRk0T7OKzY12a7MAPvRA1pLPGw4YVQkIGvRnPDxEVMmCLu2mlTU+AR1o2cVegjntYTM0Y9+lXIi0VsO5PSq96BNfFR0QVOqlExjjHWvKrtOZ30laJct2zGQzZqaCNoyeTyeMVSgJJAB2irbNJGdo5HrXObEwmIOxl6U4swbcoIUDpUIUuo7cVZUKVEe7r1qGUhyXTFdh9eaUrGy8gA96csIyAo4NNdNpI2/dpXGNeOFUDHqB0qFo1MZweQM4qw6KEy2TxxUflkjaOpFNOwrGYtjHd3LoWAfHHanHRb61+ZXLKOnNRzvNBfFowc+ta8MxvYRGWZHx1zWtzMzo7e7uH2qxOOpPShZ7uxYDBC56t3rrNMsIYIMbtzEcmnXekJdR4YA+ntUcxSRT0XXjLiNiATWz57Fz5h6VzKeHpba6EiZ2d60vt6ohik6gYq4q+xEjWiuAJcZyKc1x5T5LYFcpPqfkHcjZwagvvEgeJGU8963VNszckjr57uJVLFgKx21hEmKBu+DXG33iGZ8xhj1zWe2qyNPkngmtfYNIz9oj0dJRIfMJ4xms3V7rybdmXrisu011X2RZ4AqPV7rzIHC9+KwlGzNosxLiRpp8nlq7Dwlbgx+YVwfWuYs7RjtYjgmu88PwRpbEdKwqPoaRLd588bRsnUdQa8513S0gvP3kRKknGK9KuLm3hG12A965nX5IL5VQYJHcU6baYprQ86mtthbysOv6itOxjvEt1a3uO33GqlqVpJa3BKlsdj6Ultqk8GA43LXanZ6nK1dFnVbu5kgSK4gKENncOhrZt5bea3RVlUkKOM1jXt4moW6JGcMGyQa0n0i3nhR4j5b46qa1i23pqQ1pqU9ZjZVRuoDVlw25kj3D1q9f299bQFJX8yP19Kr2l1FFAFbrmlJ66jW2ht6TAG09cjlSRVhrJe1ZFnqNxboURPlLE11WlRLfQB34PpWt0o3I3Zjm2ZPuMR+NUp2nj1OA7jnBAzV3xB5tjchI5CqmsYTSTOpLksvQ+lNe8GxuC5uk9DTm1KSNPmTn2rKUzkj94xqwkTkkSPWVarGCsnqXTpOWrRLbnfLuyNx5NaBX93g4yKySrWzhhV6G48xVI6nrXmO7dzt0WhOsXAx161OSUQAkEmoopwXwwwB6U+XZncCKBkySKQA1TBETDdyeBVKKQK2SMkflT2n83IPy4FKwXNGO4AOOCB0xU0Rlnk27QF7ms2KQBAsYLd6nt7uV5diLgUrDuXnsGZ8I4x60z7L5TEs+SaSY3EaZXGKi2zSnl8dzzSsO5DJEgvgGAYGrrNDaoCBtNYt8JbYrJHJuPoKo3F/d3ACbWrRIhnRJrLpMEjG4E81tWd4WkAIIU1w+meZa3atN0PY12ttcRRxh5Nq5FZyXYpGld/Lau69hmvOdZ1cKz7ThhnIrvHvoZbaSNSMYrzvX9MLtJKh6V2UIXVzmqyszI/tZyeScE0vnhhkHissgrkVLC+wgE8V20p8ujOapG6uWHJdsimmMjmrAZNucjFQS3SDhea6pOK1ZjG70SBZGhbzM9K2tOuBqnydNv61zbuZOpqzpklxBNmFSfU9q8+raWx109NzvoLFYrZVJBNTjURZqUU9RWbbatHJCQxwVHNZV7fL533+DXFy66nTzaE17rjSylWJPODU+n29zPKrxhivvWLZQie7yRu+avStItktrNWZMDFZzdtEVFdzh/EllJEhSVdhPIbtWStojWqA7XwOcV3/AIpjTULMpFGC6j0riZdHkSISW0myQD5l7V2UZuSOerFI5+5j8mdlXIArRtJruFA9vLvUdVNVbpHWT/SUMb+vY1eSJWUS2zgMB271qkmzN7DrnVBdWjwTIY3Pc02wtrY2w80oWye9R386PalJIwsorKBGO9OTaeuoJXR28HhhkTDTjI9qux3DaPH5YXzPoKo2+puQTLKee1XLe9hkDE/MB610SjNL3tUYJxb0M7VJBrDAsgiK+tZ0OneXKMkEe1adzcpLJ8qgAelRLJ83IwM151Wom7R0O6nCy1LUVtEsecCk+yozdvwpUkUgA8CpSwQbl6CuNp3Oi5n3Me1WU9R60mmxhkbcDgVNdpuw/qaWzZIkcetaLYhhGuCxHQcjFLGzSOdwOfSrMaALvBHA5FJAm+VmPFJsEiKKIgEu5GelIkTyHO4lQatTwbsAHioWdkPlRincGiUTi3jbnp2FQRSSmUPHnHrUiRDafMH1FSTSRwxqsfJxTEWGuLhwq4PHX3pzJdSqcAAniq0VwIULMfmqOXUp5CwTIz0xRYdyaSBomQyNuHpW5b2NpJGHCrnFctObhyD83AxUtteXIIjVmGKGgLWtafKGDIQAO9YxvL1UaEuWHatOc6kVPnBiM8Vc0vT4nYCReWFS5WGlczNLvpY/9Yx5GOas3EkcqFW5BFbV74ehSBpF+UY5rDk+w26FftClh2JzXo4Wa5Tirxd7nL3+mOkpdAdlU1tgOtdXJPayxmPdnPTis8aPNI/yAAHpXYqcVqzn53sjFlj+TjNV1jZjtVSTXYW/hS4lwZPu+gqK5jTSJPLW2UMP4j1rKaUpWgaRbitTJsNCmnIef93H1962vNsrCAw28YdsdP8AGoE+0agh2uwHoBUkGnNbk/KST3NVGlbcmU+xhtLOJHbaQGPOO1WILZ7kAqCa2JrUtEyqgyR6VnabdNpN4RMuUJ6VlWoaXiaUqutmdToHhwxussg7V2W6KC3CsBgVk6XqcE8SsGGMVbub23XCOwwa8qUWtztTQ37RZgtlAK4fXA1rePLbL8hOSBXavBaSrvU5A64rF16CKW3UwJgjg8VVCfLOwVY3icJrE8d1HAR13YIqe50YxgPbOUbGcZqWbw5cXMwkjJGDnBFXGkuLQBLuPI6blr0tG9Ti1tocxdvMcR3SkMvRsVAIGIyrKR9a3NaMclkXXBwevcVQt9KaaBJA+NwzWcou+haloaUUZkcDtUlxcrCRCh69ailjuIIy4Urisprhi5Ynk104mquWyMKFN3uzaikQpnuKmE6bcbT9az7RwUzwT/KrQnGAMj8K8po9BFpGAGen1q5Dskj5qgGVh8x605JWgPy5IqWkx7Fq4UbAPSs4ylZCMYANaHnLIm9jj1qg6RTviOcZ/wB2t6cE1qZTk0Xre5CKcnj3qWBhvyemaz2t5tmNpPuKW3aZDhwetTOk0VComaks6qcr2pIwGYOPxqi8jbuRxU6XO1NqkZNY8rRpzJkhb5mbHGeBUMp242j5vSpBKq4GMmgMEbf1Y/pS2HuMjtpH5l49quJHFBy5HFVGnkUnLcntUDtJJkAnBo5kHKa5vICuFxz0p2nvbySEuQNpzWRHbO55O0Uqots+Hk+Q1WjJ2OnudQtSgiUg+lNt0QtnftxyK5+2sp71i9v8sIPJJya2rewmQou8c8ZJ5rOSSLial5dq+lyJ3C15jOuLlif71em3VlGunyxlgXC+teXXLOl0ykEgGvRwMorc5MTFvYvxDAB9K6ayWNoFOecVycLzMoCQufwrXt01eSIJDbN9SOlerUq00tzzoU532O4tLi3jtwXcCuP8W3NpcXStCyk98VEdF1u5UeZIyj+7nFVX8PXMT/vyQM9a5Kc6andSOmUZctmh1pqiWsIRUyR7VI+uGVuUCiq9xp6xplG6Vnn0rtWupyvQ3UuXfnIwaiutOFyRJuFMtB/oo4qxDK6/Jt4rGo2tmawSfQrwXb6XcBSjBDWpAx1WXKTEc1TuY1lQhhVbTZm0+78rcQGPBrzKsL7HdTlY7TT9Oe3/ANZKStS3iMsDeUqvikgEssIDMORwRTltpLOMyGQsO4rzWrSOvoYc1x5cOFQqx68dKy5DuOW5zWvczpJMzFDs9cVjahcKvyQRnd616+GmpKx59aLTKF6bfaYygJPWq0Vx5MYjVQAOlDQylwGU7mrds/D6NbK0vDHmuqThHc51zyZxM1zPKvzysR9ajQ5X6Vo2tkLjcoHIrOniaCZk6YNclak47nVTmnoWI5zGuFOM1ctsuASaykPNW7efHHQiuVo3TsbUYAXmkdo0P3jWd9sIGM8CkS7DkmptYdzYt5U5Ctx71GV8ufeBkD2qnE0jN8oqcSOnys1XB2dxSV0WZNaiiTBRj9KoSas8nEaY+tNli8w5GBVc2zRuGJAB962dVtGSppE/2yY8kE+1SxX7NwRtoitUlHysc1MLJUPPP0FZe1fUtQSJIpiMktn0qzHKMe9VJVhtkyW5Haqx1KNRhFOTWU1zdDWLUTWLKTnPSmLMAxY9OwFQWrpcJgHBNW1tUjXJYE1mk0U2mI1zIU4+Vahx9pIRj1NPmYA7cGpLZACCOtNMkuC/NpELW1QlvpT/ALXdRgyzEqegFXbNVDdFZj0zUy6E9xL5t3MNmchRUyaKVyXTre6vbbzJ3IyOKhTQ9OS6LTKCxNbcG0wiGMYC1k3r7b7BJyKmM5K9mNxT3L9vYWUX+rhX8qsq2w4XCj0rPivI0X722omvt7HB3YrOTb3KSRruVkH3hTVs0bKSKGRvWs6wPnXAMjcZ6V1KWqmLcDjAqFJqWhTStqeVeLdNfTbsNEzeU/TnpWInTmvQfHSQf2TliPMDfKK84WVlPtX0mEqynSXMePiaaUtDTgvTEoTGRWhb3sTLgABvesSNjJ91CfwpHmeA5KMv4VrPke7Mo8/RG7KHflTiqUysB8x5HIrPXWph8uOKeJ5bs9GJ9qySg9DRuS1O10C8N3bqGf7vBrYlclCgyRjFcz4OgiiN19p80OsZdAOnFbWj6nZ3N80EjsSVJUD1FeFiXy1JJJ6Ho0ppxTZFe4FuIUTBY8n0plrawbVDIrN9KqXGv20rsu4eYG2hRWlYXNkbiWOUgLaxb5sHkn0rGVRxjezNfdHNodvK3nogDrzisW+12HT7prZhyvWuptdQsbkWkloGMVxvyAc42jJrm72XS766ae60O6SQ8DbnkdjWmHxM3o4t/d/mYVeRbHF2jyQAyYGCO5qlfHzHyQMn0qzcWrW6/O2QKSE2bjDkg+9e9UakrNnFHR3RnFcpn0piOVbNXrnyFBSPnNUiuDiuFqzsdSdxWkIOc06GXbICaY64FR9KVkxm4l5GkfynmoftoDbmrK3ml3EDBo5QuXpL8nimQuXfLkmqealikKHg0mgTOmsJUEeCuMVI1wFBPTisKO8YKPmpst28p+Y/lUWKuWLi7+Y7CDntVEEs2elMzz1pw+tUI0LWV0IrT+1ttzt7Viw7s8VbZpxHjbwKkdyzHqCtJsdQM9604WVdrA5rlpHbd83BrQ0+eQ4UNmocSkztLREyHMZJ7Yq1cSXJUhF2DtVKymle1XyvvgYzVg3VxEh89C2OlYyNYlnTbxk+SVuTxmqHiWVrW5WTdhSvWpLO5eZtrQ4XPXHSqfi3MunKyHPlnB+lFON52FN2VzJXXFBIdtwp39uRImFrnYUBk+bpUklvI/pGh/Ou+WEtucyxHY6mw8RIkgLOoA963b34g20GneTaoZZiMZHSvPLPSZZHBCkj1Nb9tpMUYzKNx/SlDBxTuEsQ7WM6bUb3VZi1w7Y/ugVG2nygblhwBzzXSJbxR/dRR+FI6hu1d8IcqsjklPmZzcWtNYsUMC5HqKrXetS3Qw0aBfpVvWdPO4yr1rHit2c8jAFYyT5tjRNcpPbRNOwEceTXW6bH5UCiSFNw74rBs7xbUBEjGB3rfsruK4HBw3pWkoNrVEKVnodLo0Vu/nk4BMRHNYdtYrp3iGOTcFjZW5PAHFPWRo2yhxiob7VbaWIw3KEHHBrlWHalKz3Lck1qctp8YTUri9k5itd0n1bPyj860PCcv2y61CCWQCW8hZVLHGW64qD+zJLnclt80bHsa2dB8LeRMJLnt0FKpQSVmwjNy2R0OgaXPp0GnWkjLHNGJmPP3SwwK29J0q8tbLy7u53ybicuwJxVSKKOEAKMAUPdorYMmPxrz54Jzd0/wNk7bnj11I7AhnJFURwaKK9RsiIE0DpRRUSKJmjUpmqp4oorNFDaVRRRVCFIFIKKKQyRRS9BRRUlCgU9e1FFJgaVkisRkVtCGMwH5elFFStwOd1EAScVr+HLWJiGIJP1oorRrQlbnWQAQ2zeWMYPFKbmTyuSG+ooorge51oZDdytdeXwq/7IqHUlC2NyOvB60UU4aTQS+FnGRAbs+9bdjaQvGHZcn3oor3/snkPc1AqovyjGKcORRRTQmHQCqtzO8eduOKKK0RJi3F5NK5ViMZ7Cq0oCjAGKKKOoPYi71LBK8b5RiDmiirEdPaSNLArNjJFVdUhR4iSOaKKxW5ZnaTcy29wPLcgE9K7GO7lCDBHIoorCslzGtN6Gfd6tdxylVcY+lUHvp2bJbmiitoxSWhlJu5//2Q==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/jpeg": {
       "height": 256,
       "width": 256
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prediction: 1.0\n"
     ]
    },
    {
     "data": {
      "image/jpeg": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/jpeg": {
       "height": 256,
       "width": 256
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import Image, display\n",
    "for cat in samplecat:\n",
    "    print (\"prediction:\"), cat.prediction\n",
    "    display(Image(cat.image.origin[5:], height=256,width=256))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Show two dog-pics and their predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prediction: 2.0\n"
     ]
    },
    {
     "data": {
      "image/jpeg": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/jpeg": {
       "height": 256,
       "width": 256
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prediction: 2.0\n"
     ]
    },
    {
     "data": {
      "image/jpeg": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/jpeg": {
       "height": 256,
       "width": 256
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show the two pics and their things as follow\n",
    "from IPython.display import Image, display\n",
    "for dog in sampledog:\n",
    "    print (\"prediction:\"), dog.prediction\n",
    "    \n",
    "    display(Image(dog.image.origin[5:], height=256,width=256))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
